{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3f28bbab",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as mdates\n",
    "from dateutil import relativedelta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "id": "ac7ba762",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Precip</th>\n",
       "      <th>spi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1436</th>\n",
       "      <td>31-08-2021</td>\n",
       "      <td>525.1</td>\n",
       "      <td>-0.539477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1437</th>\n",
       "      <td>30-09-2021</td>\n",
       "      <td>253.4</td>\n",
       "      <td>-1.025351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1438</th>\n",
       "      <td>31-10-2021</td>\n",
       "      <td>242.7</td>\n",
       "      <td>-1.105963</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1439</th>\n",
       "      <td>30-11-2021</td>\n",
       "      <td>21.2</td>\n",
       "      <td>-1.131985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1440</th>\n",
       "      <td>31-12-2021</td>\n",
       "      <td>25.9</td>\n",
       "      <td>-1.040785</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Date  Precip       spi\n",
       "1436  31-08-2021   525.1 -0.539477\n",
       "1437  30-09-2021   253.4 -1.025351\n",
       "1438  31-10-2021   242.7 -1.105963\n",
       "1439  30-11-2021    21.2 -1.131985\n",
       "1440  31-12-2021    25.9 -1.040785"
      ]
     },
     "execution_count": 234,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('C:/Users/owner/Documents/SPI/spi_12/spi integrated/bangladesh.csv')\n",
    "df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88492858",
   "metadata": {},
   "source": [
    "Parameters Define"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "id": "b450f4c8",
   "metadata": {},
   "outputs": [],
   "source": [
    "MinTresh = -0.5\n",
    "MaxTresh = 0.5\n",
    "Event = 0\n",
    "Total = 0\n",
    "Gate = True\n",
    "SpiList = []\n",
    "minL = min(df['spi'][:])\n",
    "maxL = max(df['spi'][:])\n",
    "StartDate=[]\n",
    "EndDate=[]\n",
    "StartIntensity = []\n",
    "EndIntensity =[]\n",
    "SumSpi=[]\n",
    "MinList=[]\n",
    "Duration =[]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "47ba5489",
   "metadata": {},
   "source": [
    "# Loop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "id": "4601720d",
   "metadata": {},
   "outputs": [],
   "source": [
    "#def add(df):\n",
    "for i in range(0,len(df['spi'])):\n",
    "\n",
    "            # Min Threshold Check :    \n",
    "            if df['spi'][i]<=MinTresh:\n",
    "                if Gate == True:\n",
    "                    StartIndex = i\n",
    "                    Gate = False\n",
    "\n",
    "            # Inside Event --> Saving spi :\n",
    "            if Gate == False:\n",
    "                SpiList.append(df['spi'][i]) \n",
    "            # Max Threshold Check : \n",
    "            if df['spi'][i]>= MaxTresh or i == len(df['spi']) - 1:\n",
    "                if Gate == False:\n",
    "                    EndIndex = i\n",
    "                    if EndIndex-StartIndex >= 3:\n",
    "                        StartDate.append(df['Date'][StartIndex])\n",
    "                        EndDate.append(df['Date'][EndIndex])\n",
    "                        SumSpi.append(sum(SpiList))\n",
    "                        MinList.append(min(SpiList))\n",
    "                        #SpiList=[]\n",
    "                        Event=Event+1\n",
    "                        Gate = True\n",
    "                        minL = min(df['spi'])\n",
    "                        SpiList=[]\n",
    "#return avg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "id": "819d79ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Event = Event+1\n",
    "groups = SpiList\n",
    "# spi summation :\n",
    "SUM = sum(SpiList)\n",
    "\n",
    "# Average spi for events:\n",
    "AVR = SUM/Event\n",
    "\n",
    "#intensity = min(SpiList)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "id": "77a104c1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of 'events' =  27\n",
      "'Sumation' of spi for all events =  0\n",
      "'Average' of spi for all events =  0.0\n",
      "Intensity = -3.126353629\n"
     ]
    }
   ],
   "source": [
    "# Print results :\n",
    "print(\"Number of 'events' = \",Event)\n",
    "print(\"'Sumation' of spi for all events = \",SUM)\n",
    "print(\"'Average' of spi for all events = \",AVR)\n",
    "print(\"Intensity =\", minL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "id": "7190ad80",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "27\n",
      "27\n"
     ]
    }
   ],
   "source": [
    "print(len(EndDate))\n",
    "print(len(StartDate))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "id": "a8fb0e69",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'31-08-2018'"
      ]
     },
     "execution_count": 240,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "StartDate[-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "id": "a9cd1b90",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Event = 1\n",
      "Start Date:  31-05-1903  ~~  End Date:  31-07-1904\n",
      "Sum of Spi=  -2.4899211810000015\n",
      "Min spi=  -0.733552156\n",
      " ************************************************************ \n",
      "Event = 2\n",
      "Start Date:  30-06-1905  ~~  End Date:  30-09-1905\n",
      "Sum of Spi=  -1.2743010569999997\n",
      "Min spi=  -1.084319039\n",
      " ************************************************************ \n",
      "Event = 3\n",
      "Start Date:  30-09-1906  ~~  End Date:  31-08-1908\n",
      "Sum of Spi=  -18.842771569000003\n",
      "Min spi=  -1.628111609\n",
      " ************************************************************ \n",
      "Event = 4\n",
      "Start Date:  30-06-1910  ~~  End Date:  31-05-1911\n",
      "Sum of Spi=  3.5034624750000005\n",
      "Min spi=  -0.751439198\n",
      " ************************************************************ \n",
      "Event = 5\n",
      "Start Date:  31-07-1919  ~~  End Date:  31-10-1919\n",
      "Sum of Spi=  1.771570657\n",
      "Min spi=  -0.637883106\n",
      " ************************************************************ \n",
      "Event = 6\n",
      "Start Date:  30-09-1923  ~~  End Date:  31-07-1924\n",
      "Sum of Spi=  -4.510298092999999\n",
      "Min spi=  -0.995970691\n",
      " ************************************************************ \n",
      "Event = 7\n",
      "Start Date:  30-06-1930  ~~  End Date:  30-06-1932\n",
      "Sum of Spi=  -10.83061163\n",
      "Min spi=  -0.955596556\n",
      " ************************************************************ \n",
      "Event = 8\n",
      "Start Date:  30-06-1933  ~~  End Date:  30-09-1934\n",
      "Sum of Spi=  -3.0248698039999997\n",
      "Min spi=  -0.663301158\n",
      " ************************************************************ \n",
      "Event = 9\n",
      "Start Date:  31-07-1937  ~~  End Date:  30-06-1938\n",
      "Sum of Spi=  -4.3692099099999995\n",
      "Min spi=  -0.609738871\n",
      " ************************************************************ \n",
      "Event = 10\n",
      "Start Date:  31-08-1944  ~~  End Date:  30-09-1946\n",
      "Sum of Spi=  -12.556056528999996\n",
      "Min spi=  -1.021046811\n",
      " ************************************************************ \n",
      "Event = 11\n",
      "Start Date:  30-06-1957  ~~  End Date:  31-08-1959\n",
      "Sum of Spi=  -39.31162700099999\n",
      "Min spi=  -2.678256244\n",
      " ************************************************************ \n",
      "Event = 12\n",
      "Start Date:  30-06-1962  ~~  End Date:  30-11-1964\n",
      "Sum of Spi=  -15.059511315000002\n",
      "Min spi=  -1.260729985\n",
      " ************************************************************ \n",
      "Event = 13\n",
      "Start Date:  31-07-1966  ~~  End Date:  30-06-1968\n",
      "Sum of Spi=  -9.989836904000002\n",
      "Min spi=  -1.022481255\n",
      " ************************************************************ \n",
      "Event = 14\n",
      "Start Date:  30-06-1969  ~~  End Date:  31-10-1970\n",
      "Sum of Spi=  -4.236302149\n",
      "Min spi=  -1.280487491\n",
      " ************************************************************ \n",
      "Event = 15\n",
      "Start Date:  30-09-1971  ~~  End Date:  31-03-1974\n",
      "Sum of Spi=  -35.86876837100001\n",
      "Min spi=  -2.651108353\n",
      " ************************************************************ \n",
      "Event = 16\n",
      "Start Date:  31-08-1975  ~~  End Date:  30-06-1976\n",
      "Sum of Spi=  -4.9365469399999995\n",
      "Min spi=  -0.725843979\n",
      " ************************************************************ \n",
      "Event = 17\n",
      "Start Date:  30-04-1978  ~~  End Date:  31-05-1980\n",
      "Sum of Spi=  -18.928626983999997\n",
      "Min spi=  -1.91249071\n",
      " ************************************************************ \n",
      "Event = 18\n",
      "Start Date:  30-09-1980  ~~  End Date:  31-07-1981\n",
      "Sum of Spi=  -1.8148862870000004\n",
      "Min spi=  -0.773218372\n",
      " ************************************************************ \n",
      "Event = 19\n",
      "Start Date:  31-05-1982  ~~  End Date:  31-10-1983\n",
      "Sum of Spi=  -7.2743237139999986\n",
      "Min spi=  -1.936818293\n",
      " ************************************************************ \n",
      "Event = 20\n",
      "Start Date:  31-10-1985  ~~  End Date:  30-06-1987\n",
      "Sum of Spi=  -11.743124507000003\n",
      "Min spi=  -2.115187412\n",
      " ************************************************************ \n",
      "Event = 21\n",
      "Start Date:  31-07-1989  ~~  End Date:  31-08-1990\n",
      "Sum of Spi=  -12.209978070999998\n",
      "Min spi=  -1.794263016\n",
      " ************************************************************ \n",
      "Event = 22\n",
      "Start Date:  30-06-1992  ~~  End Date:  31-08-1993\n",
      "Sum of Spi=  -21.750161892999998\n",
      "Min spi=  -2.615829638\n",
      " ************************************************************ \n",
      "Event = 23\n",
      "Start Date:  31-07-1994  ~~  End Date:  30-11-1998\n",
      "Sum of Spi=  -47.435275057999995\n",
      "Min spi=  -3.126353629\n",
      " ************************************************************ \n",
      "Event = 24\n",
      "Start Date:  30-04-2001  ~~  End Date:  30-06-2008\n",
      "Sum of Spi=  -65.08377709099999\n",
      "Min spi=  -1.919822379\n",
      " ************************************************************ \n",
      "Event = 25\n",
      "Start Date:  31-10-2008  ~~  End Date:  31-10-2015\n",
      "Sum of Spi=  -111.18171246299997\n",
      "Min spi=  -2.440226885\n",
      " ************************************************************ \n",
      "Event = 26\n",
      "Start Date:  31-07-2016  ~~  End Date:  31-08-2017\n",
      "Sum of Spi=  -10.638795463\n",
      "Min spi=  -1.556366223\n",
      " ************************************************************ \n",
      "Event = 27\n",
      "Start Date:  31-08-2018  ~~  End Date:  31-12-2021\n",
      "Sum of Spi=  -48.43389229600001\n",
      "Min spi=  -2.927632443\n",
      " ************************************************************ \n"
     ]
    }
   ],
   "source": [
    "df1 = pd.DataFrame(EndDate,StartDate, columns=['Date'])\n",
    "for j in range(Event):\n",
    "    print(\"Event =\",j+1)\n",
    "    print(\"Start Date: \",StartDate[j], \" ~~  End Date: \", EndDate[j])\n",
    "    print(\"Sum of Spi= \",SumSpi[j])\n",
    "    print(\"Min spi= \",MinList[j])\n",
    "    print(\" ************************************************************ \")\n",
    "#df1.head(23)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "id": "16726d1c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "27"
      ]
     },
     "execution_count": 242,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(SumSpi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "id": "62b3a993",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "27"
      ]
     },
     "execution_count": 243,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(MinList)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "id": "d4ad1c12",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>StartDate</th>\n",
       "      <th>EndDate</th>\n",
       "      <th>Severity</th>\n",
       "      <th>Intensity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>31-05-1903</td>\n",
       "      <td>31-07-1904</td>\n",
       "      <td>-2.489921</td>\n",
       "      <td>-0.733552</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>30-06-1905</td>\n",
       "      <td>30-09-1905</td>\n",
       "      <td>-1.274301</td>\n",
       "      <td>-1.084319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>30-09-1906</td>\n",
       "      <td>31-08-1908</td>\n",
       "      <td>-18.842772</td>\n",
       "      <td>-1.628112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30-06-1910</td>\n",
       "      <td>31-05-1911</td>\n",
       "      <td>3.503462</td>\n",
       "      <td>-0.751439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>31-07-1919</td>\n",
       "      <td>31-10-1919</td>\n",
       "      <td>1.771571</td>\n",
       "      <td>-0.637883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>30-09-1923</td>\n",
       "      <td>31-07-1924</td>\n",
       "      <td>-4.510298</td>\n",
       "      <td>-0.995971</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>30-06-1930</td>\n",
       "      <td>30-06-1932</td>\n",
       "      <td>-10.830612</td>\n",
       "      <td>-0.955597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>30-06-1933</td>\n",
       "      <td>30-09-1934</td>\n",
       "      <td>-3.024870</td>\n",
       "      <td>-0.663301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>31-07-1937</td>\n",
       "      <td>30-06-1938</td>\n",
       "      <td>-4.369210</td>\n",
       "      <td>-0.609739</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>31-08-1944</td>\n",
       "      <td>30-09-1946</td>\n",
       "      <td>-12.556057</td>\n",
       "      <td>-1.021047</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>30-06-1957</td>\n",
       "      <td>31-08-1959</td>\n",
       "      <td>-39.311627</td>\n",
       "      <td>-2.678256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>30-06-1962</td>\n",
       "      <td>30-11-1964</td>\n",
       "      <td>-15.059511</td>\n",
       "      <td>-1.260730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>31-07-1966</td>\n",
       "      <td>30-06-1968</td>\n",
       "      <td>-9.989837</td>\n",
       "      <td>-1.022481</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>30-06-1969</td>\n",
       "      <td>31-10-1970</td>\n",
       "      <td>-4.236302</td>\n",
       "      <td>-1.280487</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>30-09-1971</td>\n",
       "      <td>31-03-1974</td>\n",
       "      <td>-35.868768</td>\n",
       "      <td>-2.651108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>31-08-1975</td>\n",
       "      <td>30-06-1976</td>\n",
       "      <td>-4.936547</td>\n",
       "      <td>-0.725844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>30-04-1978</td>\n",
       "      <td>31-05-1980</td>\n",
       "      <td>-18.928627</td>\n",
       "      <td>-1.912491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>30-09-1980</td>\n",
       "      <td>31-07-1981</td>\n",
       "      <td>-1.814886</td>\n",
       "      <td>-0.773218</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>31-05-1982</td>\n",
       "      <td>31-10-1983</td>\n",
       "      <td>-7.274324</td>\n",
       "      <td>-1.936818</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>31-10-1985</td>\n",
       "      <td>30-06-1987</td>\n",
       "      <td>-11.743125</td>\n",
       "      <td>-2.115187</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>31-07-1989</td>\n",
       "      <td>31-08-1990</td>\n",
       "      <td>-12.209978</td>\n",
       "      <td>-1.794263</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>30-06-1992</td>\n",
       "      <td>31-08-1993</td>\n",
       "      <td>-21.750162</td>\n",
       "      <td>-2.615830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>31-07-1994</td>\n",
       "      <td>30-11-1998</td>\n",
       "      <td>-47.435275</td>\n",
       "      <td>-3.126354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>30-04-2001</td>\n",
       "      <td>30-06-2008</td>\n",
       "      <td>-65.083777</td>\n",
       "      <td>-1.919822</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>31-10-2008</td>\n",
       "      <td>31-10-2015</td>\n",
       "      <td>-111.181712</td>\n",
       "      <td>-2.440227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>31-07-2016</td>\n",
       "      <td>31-08-2017</td>\n",
       "      <td>-10.638795</td>\n",
       "      <td>-1.556366</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>31-08-2018</td>\n",
       "      <td>31-12-2021</td>\n",
       "      <td>-48.433892</td>\n",
       "      <td>-2.927632</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     StartDate     EndDate    Severity  Intensity\n",
       "0   31-05-1903  31-07-1904   -2.489921  -0.733552\n",
       "1   30-06-1905  30-09-1905   -1.274301  -1.084319\n",
       "2   30-09-1906  31-08-1908  -18.842772  -1.628112\n",
       "3   30-06-1910  31-05-1911    3.503462  -0.751439\n",
       "4   31-07-1919  31-10-1919    1.771571  -0.637883\n",
       "5   30-09-1923  31-07-1924   -4.510298  -0.995971\n",
       "6   30-06-1930  30-06-1932  -10.830612  -0.955597\n",
       "7   30-06-1933  30-09-1934   -3.024870  -0.663301\n",
       "8   31-07-1937  30-06-1938   -4.369210  -0.609739\n",
       "9   31-08-1944  30-09-1946  -12.556057  -1.021047\n",
       "10  30-06-1957  31-08-1959  -39.311627  -2.678256\n",
       "11  30-06-1962  30-11-1964  -15.059511  -1.260730\n",
       "12  31-07-1966  30-06-1968   -9.989837  -1.022481\n",
       "13  30-06-1969  31-10-1970   -4.236302  -1.280487\n",
       "14  30-09-1971  31-03-1974  -35.868768  -2.651108\n",
       "15  31-08-1975  30-06-1976   -4.936547  -0.725844\n",
       "16  30-04-1978  31-05-1980  -18.928627  -1.912491\n",
       "17  30-09-1980  31-07-1981   -1.814886  -0.773218\n",
       "18  31-05-1982  31-10-1983   -7.274324  -1.936818\n",
       "19  31-10-1985  30-06-1987  -11.743125  -2.115187\n",
       "20  31-07-1989  31-08-1990  -12.209978  -1.794263\n",
       "21  30-06-1992  31-08-1993  -21.750162  -2.615830\n",
       "22  31-07-1994  30-11-1998  -47.435275  -3.126354\n",
       "23  30-04-2001  30-06-2008  -65.083777  -1.919822\n",
       "24  31-10-2008  31-10-2015 -111.181712  -2.440227\n",
       "25  31-07-2016  31-08-2017  -10.638795  -1.556366\n",
       "26  31-08-2018  31-12-2021  -48.433892  -2.927632"
      ]
     },
     "execution_count": 244,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#df1 = pd.DataFrame(StartDate,EndDate, columns=['Date'])\n",
    "\n",
    "df1 = pd.DataFrame({'StartDate': StartDate\n",
    "                    ,'EndDate': EndDate\n",
    "                    ,'Severity': SumSpi\n",
    "                    ,'Intensity': MinList\n",
    "                   })\n",
    "df1.head(Event)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "id": "bf1ac708",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    StartDate    EndDate    Severity  Intensity   Duration\n",
      "0  1903-05-31 1904-07-31   -2.489921  -0.733552  14.000000\n",
      "1  1905-06-30 1905-09-30   -1.274301  -1.084319   3.016393\n",
      "2  1906-09-30 1908-08-31  -18.842772  -1.628112  22.983607\n",
      "3  1910-06-30 1911-05-31    3.503462  -0.751439  10.983607\n",
      "4  1919-07-31 1919-10-31    1.771571  -0.637883   3.016393\n",
      "5  1923-09-30 1924-07-31   -4.510298  -0.995971  10.000000\n",
      "6  1930-06-30 1932-06-30  -10.830612  -0.955597  23.967213\n",
      "7  1933-06-30 1934-09-30   -3.024870  -0.663301  14.983607\n",
      "8  1937-07-31 1938-06-30   -4.369210  -0.609739  10.950820\n",
      "9  1944-08-31 1946-09-30  -12.556057  -1.021047  24.918033\n",
      "10 1957-06-30 1959-08-31  -39.311627  -2.678256  25.967213\n",
      "11 1962-06-30 1964-11-30  -15.059511  -1.260730  28.983607\n",
      "12 1966-07-31 1968-06-30   -9.989837  -1.022481  22.950820\n",
      "13 1969-06-30 1970-10-31   -4.236302  -1.280487  16.000000\n",
      "14 1971-09-30 1974-03-31  -35.868768  -2.651108  29.934426\n",
      "15 1975-08-31 1976-06-30   -4.936547  -0.725844   9.967213\n",
      "16 1978-04-30 1980-05-31  -18.928627  -1.912491  24.983607\n",
      "17 1980-09-30 1981-07-31   -1.814886  -0.773218   9.967213\n",
      "18 1982-05-31 1983-10-31   -7.274324  -1.936818  16.983607\n",
      "19 1985-10-31 1987-06-30  -11.743125  -2.115187  19.901639\n",
      "20 1989-07-31 1990-08-31  -12.209978  -1.794263  12.983607\n",
      "21 1992-06-30 1993-08-31  -21.750162  -2.615830  14.000000\n",
      "22 1994-07-31 1998-11-30  -47.435275  -3.126354  51.901639\n",
      "23 2001-04-30 2008-06-30  -65.083777  -1.919822  85.836066\n",
      "24 2008-10-31 2015-10-31 -111.181712  -2.440227  83.803279\n",
      "25 2016-07-31 2017-08-31  -10.638795  -1.556366  12.983607\n",
      "26 2018-08-31 2021-12-31  -48.433892  -2.927632  39.934426\n"
     ]
    }
   ],
   "source": [
    "\n",
    "df1['StartDate'] = pd.to_datetime(df1['StartDate'], format='%d-%m-%Y')\n",
    "df1['EndDate'] = pd.to_datetime(df1['EndDate'], format='%d-%m-%Y')\n",
    "\n",
    "\n",
    "df1['Duration'] = df1['EndDate'].sub(df1['StartDate']).dt.days/30.5\n",
    "Duration = df1['Duration']\n",
    "\n",
    "print (df1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "id": "33b45234",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>StartDate</th>\n",
       "      <th>EndDate</th>\n",
       "      <th>Severity</th>\n",
       "      <th>Intensity</th>\n",
       "      <th>Duration(months)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>31-05-1903</td>\n",
       "      <td>31-07-1904</td>\n",
       "      <td>-2.489921</td>\n",
       "      <td>-0.733552</td>\n",
       "      <td>14.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>30-06-1905</td>\n",
       "      <td>30-09-1905</td>\n",
       "      <td>-1.274301</td>\n",
       "      <td>-1.084319</td>\n",
       "      <td>3.016393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>30-09-1906</td>\n",
       "      <td>31-08-1908</td>\n",
       "      <td>-18.842772</td>\n",
       "      <td>-1.628112</td>\n",
       "      <td>22.983607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>30-06-1910</td>\n",
       "      <td>31-05-1911</td>\n",
       "      <td>3.503462</td>\n",
       "      <td>-0.751439</td>\n",
       "      <td>10.983607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>31-07-1919</td>\n",
       "      <td>31-10-1919</td>\n",
       "      <td>1.771571</td>\n",
       "      <td>-0.637883</td>\n",
       "      <td>3.016393</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>30-09-1923</td>\n",
       "      <td>31-07-1924</td>\n",
       "      <td>-4.510298</td>\n",
       "      <td>-0.995971</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>30-06-1930</td>\n",
       "      <td>30-06-1932</td>\n",
       "      <td>-10.830612</td>\n",
       "      <td>-0.955597</td>\n",
       "      <td>23.967213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>30-06-1933</td>\n",
       "      <td>30-09-1934</td>\n",
       "      <td>-3.024870</td>\n",
       "      <td>-0.663301</td>\n",
       "      <td>14.983607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>31-07-1937</td>\n",
       "      <td>30-06-1938</td>\n",
       "      <td>-4.369210</td>\n",
       "      <td>-0.609739</td>\n",
       "      <td>10.950820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>31-08-1944</td>\n",
       "      <td>30-09-1946</td>\n",
       "      <td>-12.556057</td>\n",
       "      <td>-1.021047</td>\n",
       "      <td>24.918033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>30-06-1957</td>\n",
       "      <td>31-08-1959</td>\n",
       "      <td>-39.311627</td>\n",
       "      <td>-2.678256</td>\n",
       "      <td>25.967213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>30-06-1962</td>\n",
       "      <td>30-11-1964</td>\n",
       "      <td>-15.059511</td>\n",
       "      <td>-1.260730</td>\n",
       "      <td>28.983607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>31-07-1966</td>\n",
       "      <td>30-06-1968</td>\n",
       "      <td>-9.989837</td>\n",
       "      <td>-1.022481</td>\n",
       "      <td>22.950820</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>30-06-1969</td>\n",
       "      <td>31-10-1970</td>\n",
       "      <td>-4.236302</td>\n",
       "      <td>-1.280487</td>\n",
       "      <td>16.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>30-09-1971</td>\n",
       "      <td>31-03-1974</td>\n",
       "      <td>-35.868768</td>\n",
       "      <td>-2.651108</td>\n",
       "      <td>29.934426</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>31-08-1975</td>\n",
       "      <td>30-06-1976</td>\n",
       "      <td>-4.936547</td>\n",
       "      <td>-0.725844</td>\n",
       "      <td>9.967213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>30-04-1978</td>\n",
       "      <td>31-05-1980</td>\n",
       "      <td>-18.928627</td>\n",
       "      <td>-1.912491</td>\n",
       "      <td>24.983607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>30-09-1980</td>\n",
       "      <td>31-07-1981</td>\n",
       "      <td>-1.814886</td>\n",
       "      <td>-0.773218</td>\n",
       "      <td>9.967213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>31-05-1982</td>\n",
       "      <td>31-10-1983</td>\n",
       "      <td>-7.274324</td>\n",
       "      <td>-1.936818</td>\n",
       "      <td>16.983607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>31-10-1985</td>\n",
       "      <td>30-06-1987</td>\n",
       "      <td>-11.743125</td>\n",
       "      <td>-2.115187</td>\n",
       "      <td>19.901639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>31-07-1989</td>\n",
       "      <td>31-08-1990</td>\n",
       "      <td>-12.209978</td>\n",
       "      <td>-1.794263</td>\n",
       "      <td>12.983607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>30-06-1992</td>\n",
       "      <td>31-08-1993</td>\n",
       "      <td>-21.750162</td>\n",
       "      <td>-2.615830</td>\n",
       "      <td>14.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>31-07-1994</td>\n",
       "      <td>30-11-1998</td>\n",
       "      <td>-47.435275</td>\n",
       "      <td>-3.126354</td>\n",
       "      <td>51.901639</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>30-04-2001</td>\n",
       "      <td>30-06-2008</td>\n",
       "      <td>-65.083777</td>\n",
       "      <td>-1.919822</td>\n",
       "      <td>85.836066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>31-10-2008</td>\n",
       "      <td>31-10-2015</td>\n",
       "      <td>-111.181712</td>\n",
       "      <td>-2.440227</td>\n",
       "      <td>83.803279</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>31-07-2016</td>\n",
       "      <td>31-08-2017</td>\n",
       "      <td>-10.638795</td>\n",
       "      <td>-1.556366</td>\n",
       "      <td>12.983607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>31-08-2018</td>\n",
       "      <td>31-12-2021</td>\n",
       "      <td>-48.433892</td>\n",
       "      <td>-2.927632</td>\n",
       "      <td>39.934426</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     StartDate     EndDate    Severity  Intensity  Duration(months)\n",
       "0   31-05-1903  31-07-1904   -2.489921  -0.733552         14.000000\n",
       "1   30-06-1905  30-09-1905   -1.274301  -1.084319          3.016393\n",
       "2   30-09-1906  31-08-1908  -18.842772  -1.628112         22.983607\n",
       "3   30-06-1910  31-05-1911    3.503462  -0.751439         10.983607\n",
       "4   31-07-1919  31-10-1919    1.771571  -0.637883          3.016393\n",
       "5   30-09-1923  31-07-1924   -4.510298  -0.995971         10.000000\n",
       "6   30-06-1930  30-06-1932  -10.830612  -0.955597         23.967213\n",
       "7   30-06-1933  30-09-1934   -3.024870  -0.663301         14.983607\n",
       "8   31-07-1937  30-06-1938   -4.369210  -0.609739         10.950820\n",
       "9   31-08-1944  30-09-1946  -12.556057  -1.021047         24.918033\n",
       "10  30-06-1957  31-08-1959  -39.311627  -2.678256         25.967213\n",
       "11  30-06-1962  30-11-1964  -15.059511  -1.260730         28.983607\n",
       "12  31-07-1966  30-06-1968   -9.989837  -1.022481         22.950820\n",
       "13  30-06-1969  31-10-1970   -4.236302  -1.280487         16.000000\n",
       "14  30-09-1971  31-03-1974  -35.868768  -2.651108         29.934426\n",
       "15  31-08-1975  30-06-1976   -4.936547  -0.725844          9.967213\n",
       "16  30-04-1978  31-05-1980  -18.928627  -1.912491         24.983607\n",
       "17  30-09-1980  31-07-1981   -1.814886  -0.773218          9.967213\n",
       "18  31-05-1982  31-10-1983   -7.274324  -1.936818         16.983607\n",
       "19  31-10-1985  30-06-1987  -11.743125  -2.115187         19.901639\n",
       "20  31-07-1989  31-08-1990  -12.209978  -1.794263         12.983607\n",
       "21  30-06-1992  31-08-1993  -21.750162  -2.615830         14.000000\n",
       "22  31-07-1994  30-11-1998  -47.435275  -3.126354         51.901639\n",
       "23  30-04-2001  30-06-2008  -65.083777  -1.919822         85.836066\n",
       "24  31-10-2008  31-10-2015 -111.181712  -2.440227         83.803279\n",
       "25  31-07-2016  31-08-2017  -10.638795  -1.556366         12.983607\n",
       "26  31-08-2018  31-12-2021  -48.433892  -2.927632         39.934426"
      ]
     },
     "execution_count": 246,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "df2 = pd.DataFrame({'StartDate': StartDate\n",
    "                    ,'EndDate': EndDate\n",
    "                    ,'Severity': SumSpi\n",
    "                    ,'Intensity': MinList\n",
    "                    ,'Duration(months)': Duration\n",
    "                   })\n",
    "df2.head(Event)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "id": "99073566",
   "metadata": {},
   "outputs": [],
   "source": [
    "#df2.to_csv('C:/Users/owner/Documents/SPI/Drought event/Africa/Mozambique.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "id": "ca9dc6da",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Total Months between two dates is: 14\n"
     ]
    }
   ],
   "source": [
    "from datetime import datetime\n",
    "from dateutil import relativedelta\n",
    "\n",
    "# get two dates\n",
    "d1 =\"01-12-1901\"\n",
    "d2 =\"01-02-1903\"  \n",
    "\n",
    "# convert string to date object\n",
    "start_date = datetime.strptime(d1, \"%d-%m-%Y\")\n",
    "end_date = datetime.strptime(d2, \"%d-%m-%Y\")\n",
    "\n",
    "         \n",
    "# Get the relativedelta between two dates\n",
    "delta = relativedelta.relativedelta(end_date, start_date)\n",
    "res_months = delta.months + (delta.years * 12)\n",
    "print('Total Months between two dates is:', res_months)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 249,
   "id": "a9880652",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    720 days\n",
      "1    730 days\n",
      "2   1096 days\n",
      "3    732 days\n",
      "4   2185 days\n",
      "dtype: timedelta64[ns]\n"
     ]
    }
   ],
   "source": [
    "df2 = pd.DataFrame()\n",
    "    \n",
    "df2[\"Date1\"] = [pd.Timestamp(\"01-12-1901\"),\n",
    "                     pd.Timestamp(\"01-12-1903\"),pd.Timestamp(\"01-05-1909\"),pd.Timestamp(\"01-07-1913\"),pd.Timestamp(\"01-10-1921\")]\n",
    "df2[\"Date2\"]    = [pd.Timestamp(\"01-02-1903\"),\n",
    "                     pd.Timestamp(\"01-11-1905\"),pd.Timestamp(\"01-06-1912\"),pd.Timestamp(\"01-09-1915\"),pd.Timestamp(\"01-04-1927\")] \n",
    "print(df2[\"Date2\"] - df2[\"Date1\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "id": "a335ad19",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DatetimeIndex(['1901-12-01', '1902-01-01', '1902-02-01', '1902-03-01',\n",
       "               '1902-04-01', '1902-05-01', '1902-06-01', '1902-07-01',\n",
       "               '1902-08-01', '1902-09-01',\n",
       "               ...\n",
       "               '2021-03-01', '2021-04-01', '2021-05-01', '2021-06-01',\n",
       "               '2021-07-01', '2021-08-01', '2021-09-01', '2021-10-01',\n",
       "               '2021-11-01', '2021-12-01'],\n",
       "              dtype='datetime64[ns]', length=1441, freq='MS')"
      ]
     },
     "execution_count": 250,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.date_range('1901-12-01', '2021-12-01', freq='MS')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 251,
   "id": "fe18c6cb",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['DATE'] = pd.date_range('1901-12-01', '2021-12-01', freq='MS')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "id": "5fdc0716",
   "metadata": {},
   "outputs": [],
   "source": [
    "date =  pd.date_range('1901-12-01', '1950-12-01', freq='MS')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "id": "2e5a0d47",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Event = Event+1\n",
    "groups = SpiList\n",
    "# spi summation :\n",
    "SUM = sum(SpiList)\n",
    "\n",
    "# Average spi for events:\n",
    "AVR = SUM/Event\n",
    "\n",
    "#intensity = min(SpiList)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 254,
   "id": "2fd4ec2a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of 'events' =  27\n",
      "'Sumation' of spi for all events =  0\n",
      "'Average' of spi for all events =  0.0\n",
      "Intensity = -3.126353629\n"
     ]
    }
   ],
   "source": [
    "# Print results :\n",
    "print(\"Number of 'events' = \",Event)\n",
    "print(\"'Sumation' of spi for all events = \",SUM)\n",
    "print(\"'Average' of spi for all events = \",AVR)\n",
    "print(\"Intensity =\", minL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "id": "55603e28",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15, 5))\n",
    "ax.step(df['DATE'][-200:-150], df['spi'][-200:-150],  color='grey')\n",
    "ax.axhline(-0.5, linestyle='--', color='red')\n",
    "ax.axhline(.0, linestyle='-', color='black')\n",
    "ax.axhline(0.5, linestyle='--', color='blue')\n",
    "ax.set_ylabel('SPI12',fontsize=18)\n",
    "#ax.set_title('')\n",
    "\n",
    "# Increase the size of x-axis and y-axis tick marks\n",
    "ax.tick_params(axis='x', labelsize=18)  # Change 14 to your desired size\n",
    "ax.tick_params(axis='y', labelsize=18)  # Change 14 to your desired size\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "5725b537",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(15, 5))\n",
    "\n",
    "# Plot the step line\n",
    "ax.step(df['DATE'][-300:], df['spi'][-300:], color='grey')\n",
    "\n",
    "# Add horizontal lines\n",
    "ax.axhline(-0.5, linestyle='--', color='red')\n",
    "ax.axhline(0.0, linestyle='-', color='grey')\n",
    "ax.axhline(0.5, linestyle='--', color='blue')\n",
    "\n",
    "# Add title\n",
    "ax.set_title('USA')\n",
    "\n",
    "# Annotate three specific points (example points chosen arbitrarily)\n",
    "ax.annotate('Point 1', xy=(df['DATE'].iloc[-300+50], df['spi'].iloc[-300+50]), xytext=(df['DATE'].iloc[-300+50], df['spi'].iloc[-300+50]+0.5),\n",
    "             arrowprops=dict(facecolor='black', arrowstyle='->'))\n",
    "ax.annotate('Point 2', xy=(df['DATE'].iloc[-300+150], df['spi'].iloc[-300+150]), xytext=(df['DATE'].iloc[-300+150], df['spi'].iloc[-300+150]+0.5),\n",
    "             arrowprops=dict(facecolor='black', arrowstyle='->'))\n",
    "ax.annotate('Point 3', xy=(df['DATE'].iloc[-300+250], df['spi'].iloc[-300+250]), xytext=(df['DATE'].iloc[-300+250], df['spi'].iloc[-300+250]+0.5),\n",
    "             arrowprops=dict(facecolor='black', arrowstyle='->'))\n",
    "\n",
    "# Adding hatching to specific areas\n",
    "ax.fill_between(df['DATE'][-300:], df['spi'][-300:], 0.5, where=(df['spi'][-300:] >= 0.5), facecolor='blue', alpha=0.3, hatch='//')\n",
    "ax.fill_between(df['DATE'][-300:], df['spi'][-300:], -0.5, where=(df['spi'][-300:] <= -0.5), facecolor='red', alpha=0.3, hatch='\\\\')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ac084176",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
